http://speclab.cr.usgs.gov

Mapping Minerals with Imaging Spectroscopy

Roger N. Clark, Gregg A. Swayze, and Andrea Gallagher(1)

U.S. Geological Survey

Mail Stop 964

Box 25046 Federal Center

Denver, CO 80225

(1) Now with PCI.

Imaging spectroscopy is a new mapping tool and the next generation in
remote sensing technology. The narrow spectral channels of an imaging
spectrometer form a continuous reflectance spectrum of the Earth's surface,
which contrasts with the 4 to 7 channels of the previous generation of
imaging instruments, like the Landsat Thematic Mapper (TM) and
Multispectral Scanner (MSS) instruments. While systems like Landsat can
distinguish general brightness and slope differences in the reflectance
spectrum of the surface, imaging spectroscopy not only does that, but
also resolves absorption bands in the spectrum which can be used to
identify specific species. Spectroscopic analysis of imaging
spectroscopy data allows any material (mineral, vegetation, man-made,
water, snow, etc.) with unique absorption features in the measured spectral
region to be mapped.

NASA is now flying the "Airborne Visual and Infra-Red Imaging
Spectrometer" (AVIRIS) instrument. AVIRIS acquires data in the spectral
range from 0.4 to 2.45 microns in 224 spectral channels. The instrument
is flown in an ER-2 aircraft (a modified U-2 spy plane) at 19,800 meters
(65,000 feet). The ground resolution is 20 meters, the swath width
about 11 kilometers (614 pixels) and the swath length can be up to about
1000 kilometers. After initial poor performance in 1987 and 1989, the
AVIRIS instrument now produces superb signal-to-noise data.
(Editor's note: since this paper was published, AVIRIS has continued to improve each
year, and is now a spectacular instriment.
See Evolution in
Imaging Spectrososcopy Analysis and Sensor Signal-to-Noise: An
Examination of How Far We Have Come

In 1989 we developed a new analysis algorithm that uses a digital spectral
library of known materials and a fast, modified-least-squares method of
determining if a single spectral feature for a given material is present
(Clark et al., 1990).
We have made a major advance in the mapping algorithm: now
multiple minerals using multiple spectral features are mapped
simultaneously. This is done by a modified-least-squares fit of
spectral features from data in our digital spectral library
to corresponding spectral features in the image data. The algorithm
does not force a detection like many other algorithms in use. For example,
many algorithms take a set of curves and best fit them to the
observed data, often requiring a set of parameters (like mineral
fraction) to sum to one. Our algorithm only produces values indicating
the presence of those minerals we choose to map.
If the minerals do not exist in that area, the algorithm produces
zeros, indicating they are not detected.

Our mapping algorithm produces for each pixel in the
image, a spectral feature depth (correlated to abundance)
and a fit number (least-squares correlation coefficient) to the
reference spectra (giving a measure of confidence in the result) for
each mineral mapped. The
depth values for each pixel and each mineral form a set of images of the
minerals correlating to abundance. The fit values form a set of images
corresponding to the confidence level of the identification.
We combine single mineral fit and depth images
from several minerals, assigning a color to each mineral map. In
this way we produce multi-mineral maps. For
example, red might be assigned to hematite, where shades of red (from
brighter red indicating a stronger spectral signature. In these
maps of minerals, black indicates none of the given set of minerals
were detected at that location.

We have used the algorithm on AVIRIS data of Cuprite, Nevada
to illustrate some of the mapping possibilities with the new
generation of sensors. The geologic and alteration maps are shown
in Figures 1 and 2. A false color image of Cuprite (like one that
might be produced by broad-band remote sensing instruments) is shown in
Image A. Example minerals maps are shown for iron bearing minerals
(Image B). A color mineral map of clays and sulfates is shown in Image
C.

Geologic map of the Cuprite, Nevada mining district. The map was
produced by conventional field work combined with remote sensing TM
data. From Abrams and Ashley (1980) and Ashley and Evarts (1976) as
modified by Hook (1990).

Alteration map of the Cuprite, Nevada mining district.
The map was produced by conventional field work combined with remote
sensing TM data.
From Abrams and Ashley (1980) and Ashley and Evarts (1976) as modified
by Hook (1990).

A color infrared image of Cuprite, Nevada from AVIRIS
data. North is up in this 11 km wide by 14 km long scene.
Ground resolution is 20 meters. The linear feature running
north-south to the right of center in the image is
Highway 95.

The area to the east of the road is the well-studied Cuprite mining
district. This area consists of hydrothermally-altered volcanic rocks
and contains an intensely altered central silica cap surrounded by less
altered zones of opalized and argillized rock. The area west of the
highway consists of altered volcanic rocks, Cambrian siltstones and
limestones. It also contains silicified, opalized, and argillized zones.
Altered siltstones comprise most of the altered rocks in the northern
part of the Cuprite Hills with limestones further south.

(Editor's note: this image is actually from AVIRIS Cuprite 1993 data, so is slightly
to the east of the 1990 data set. The playa at east center is off the image in the
1990 data.)

We can detect very subtle spectral differences, like degrees of
kaolinite crystallinity (see Image D), the difference between
Na-montmorillonite versus Ca-Montmorillonite (see Image C), and
individual members of the Na-K alunite solid solution series (Image
E1-E2). At Cuprite,
maps of these subtle differences depict the alteration zone remarkably
well. For example, poorly crystalline kaolinite or halloysite seems to
be a general weathering product that occurs throughout the image, but
as one moves closer to alteration zones, the kaolinite becomes
progressively more crystalline. The highly crystalline kaolinite occurs
just outside of the alunite zones. Alunite zones can be subdivided
spectrally into areas where K-alunite occurs at the center and is
surrounded by Na-alunite (see Image E1-E2).

These mineral maps have the potential to be extraordinary tools for
mineral exploration because they show variations in mineral
chemistry, and hence, pressure, temperature, and chemical gradients
in areal detail never seen before. Coupled with further geochemical
research, detection of spectral variations in mineral solid solution
series may provide a means to map temperature gradients on a large
scale in a matter of hours. This information can also be used to
locate areas where critical relationships need investigation. The
applications seem boundless.

We have developed our methodology of calibration and mapping
analysis to be a near routine method. Imaging
spectroscopy could now be used in USGS projects, and we believe the
results illustrated here show that imaging spectroscopy could
greatly enhance USGS mineral mapping. We also feel the method could
be used for environmental problems as the spectral mapping algorithm
will work on any material having diagnostic spectral absorption or
emission features. Imaging spectroscopy could also be used in
laboratory analysis of hand samples, or in the field for
investigating small areas.

Ca-montmorillonite is LIGHT BLUE and occurs in the northeastern
portion of the scene.

Na-montmorillonite is BLUE and occurs in rock units and as loess
accumulations on alluvial fans and in playas. Some muscovites have
a similar spectral signature and are also mapped as
montmorillonite (with further research these minerals may be
separated).

Buddingtonite is PURPLE, and is located only in a few pixels east of
the highway, at and near "buddingtonite bump." On the HP paintjet
image, buddingtonite bump can be found 34mm from the right edge and
118mm from the top edge of the image. Buddingtonite bump can be
better seen in white on the expanded IMAGE E2.

Paragonite is MAGENTA, occurring mostly in the lower left center of
the image. Chlorite occurs as an intimate mixture with the
paragonite.

Opalized tuff is WHITE and occurs in the lower left corner as well
as in the central region of the Cuprite alteration zone.

Well crystalline kaolinite is BLUE and occurs near the outer margins
of the alunite areas (which are black in this image).

Medium crystalline kaolinite is GREEN and occurs between the
highly crystalline and poorly crystalline kaolinite.

Poorly crystalline kaolinite is RED and occurs furthest from the alunite
areas. Poorly crystalline kaolinite or halloysite may be due to
weathering processes. (Halloysite and poorly crystalline kaolinite are
spectrally indistinguishable at 2.2 microns.)